Intel Xeon CPU
Intel Xeon has extraordinary processing power and speed, which is very suitable for running deep learning frameworks. So you can totally use our Intel-Xeon-powered GPU Servers for MXNet.
Basic MXNet GPU
Professional MXNet GPU
Advanced MXNet GPU
Advanced MXNet GPU
Enterprise MXNet GPU
Enterprise MXNet GPU
Intel Xeon CPU
SSD-Based Drives
Full Root/Admin Access
99.9% Uptime Guarantee
Dedicated IP
DDoS Protection
User-Friendly and Easy-to-Use
Hybrid Front-End
Rich Ecosystem
Distributed Training
Efficiency and Flexibility
10+ Language Bindings
Features | MXNet | Keras | PyTorch | TensorFlow |
---|---|---|---|---|
API Level | High and low | High | Low | High and low |
Architecture | Complex, less readable | Simple, concise, readable | Complex, less readable | Not easy to use |
Datasets | Large datasets, high performance | Smaller datasets | Large datasets, high performance | Large datasets, high performance |
Debugging | Hard to debug pure symbol codes | Simple network, so debugging is not often needed | Good debugging capabilities | Difficult to conduct debugging |
Trained Models Included | Yes | Yes | Yes | Yes |
Popularity | Fourth most popular | Most popular | Third most popular | Second most popular |
Speed | Fastest on ResNet-50, high performance | Slow, low performance | Fastest on Faster-RCNN, high performance | Fastest on VGG-16, high performance |
Written In | C++, Python | Python | Lua, LuaJIT, C, CUDA, and C++ | C++, CUDA, Python |
Sample: pip install --upgrade pip pip install mxnet-cu112
Sample: # Python with GPU, use mx.gpu(), to set MXNet context to be GPUs import mxnet as mx a = mx.nd.ones((2, 3), mx.gpu()) b = a * 2 + 1 b.asnumpy() # If you don't get an import error, then MXNet is ready for python.